Understanding Micro Service – Part 9 – Observability in Microservices
Part 9: Observability — How You Understand a Distributed System in Production 1. Why Debugging Changes Completely in Microservices One […]
Part 9: Observability — How You Understand a Distributed System in Production 1. Why Debugging Changes Completely in Microservices One […]
Part 8: Real Production Failures — What Actually Breaks in Microservices Systems 1. Why Production Failures Never Look Like Architecture
Part 7: Migration Strategy — How Systems Actually Move from Monolith to Microservices 1. Why Migration Is Harder Than Building
Part 6: Performance & Scalability — What Actually Happens at Runtime 1. Why Performance Feels Different in Microservices By the
Part 5: Data Consistency — Why It Becomes the Hardest Problem in Micro Services 1. The Moment Consistency Stops Being
Part 4: Design Patterns — How Microservices Actually Survive in Production 1. Why Patterns Become Necessary (Not Optional) By the
Part 3: How Systems Actually Fail — The Reality of Distributed Failures 1. When Everything Works… Until It Doesn’t By
After understanding The Breaking Point — Why Monoliths Fail at Scale lets take a look at next step Part 2:
Part 1: The Breaking Point — Why Monoliths Fail at Scale 1. Architectural Context A monolith works well in the
Production Problem: Latency Spike Caused by a “Simple” HashMap A payment aggregation service handling tens of thousands of transactions per
Java Thread Creation Has Changed (And Most Developers Haven’t Noticed) For years, we’ve been taught: But now? Java has introduced
If you’ve worked with backend systems, you’ve already used concurrency—whether you realized it or not.
But most developers face this gap:
– They know the terms
– But don’t fully understand why problems happen and how to reason about them
This blog bridges that gap.